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Multiple thresholds and probabilities |
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Darren posted on Wednesday, September 25, 2013 - 4:50 am
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I am using the R package MplusAutomation to help visualize results from a latent class analysis that looks like this: %overall% [bdyct24$1 bdyct49$1] ; [bdyct24$2 bdyct49$2] ; [bdyct24$3 bdyct49$3] ; With relevant output: Thresholds BDYCT24$1 -0.999 BDYCT24$2 2.824 BDYCT24$3 4.837 BDYCT49$1 -1.588 BDYCT49$2 2.405 BDYCT49$3 4.263 BDYCT24 Category 1 0.269 Category 2 0.675 Category 3 0.048 Category 4 0.008 BDYCT49 Category 1 0.170 Category 2 0.748 Category 3 0.069 Category 4 0.014 MplusAutomation only imports the threshold values, so I need to know how to convert these to probabilities. I can do this for binary variables with 1 threshold, but can't seem to work it out for variables with more than 2 categories (and this more than 1 threshold). I've tried to find out how from chapter 14, but can't make it work. Many thanks. This link gives an example of the calculation for a binary variable, but seems to suggest you can't do the same thing for >2 categories. http://www.ats.ucla.edu/stat/mplus/code/lca_example2.htm |
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At the top of page 493 of the user's guide, you will find the formulas for probit regression. You can generalize these to logisitic regression using F (t) = 1 / 1 + exp (-t) |
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Darren posted on Wednesday, September 25, 2013 - 1:08 pm
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Perfect, thank you Linda. It's page 441 from the v6 online version. a <- 1/(1+exp ( 0.999)) b <- 1/(1+exp (-2.824)) c <- 1/(1+exp (-4.837)) d <- 1/(1+exp ( 4.837)) a [1] 0.2691381 b-a [1] 0.674821 c-b [1] 0.04817255 d [1] 0.007868408 |
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That looks right. |
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